code: def color_from_dim(dataarray, dim_name):
      """Return colors and color mapping of a DataArray using coord values as color code.
  
      Parameters
      ----------
      dataarray : xarray.DataArray
      dim_name : str
      dimension whose coordinates will be used as color code.
  
      Returns
      -------
      colors : array of floats
          Array of colors (as floats for use with a cmap) for each element in the dataarray.
      color_mapping : mapping coord_value -> float
          Mapping from coord values to corresponding color
      """
      present_dims = dataarray.dims
      coord_values = dataarray[dim_name].values
      unique_coords = set(coord_values)
      color_mapping = {
          coord: num / len(unique_coords)
          for num, coord in enumerate(unique_coords)
      }
      if len(present_dims) > 1:
          multi_coords = dataarray.coords.to_index()
          coord_idx = present_dims.index(dim_name)
          colors = [
              color_mapping[coord[coord_idx]] for coord in multi_coords
          ]
      else:
          colors = [color_mapping[coord] for coord in coord_values]
      return colors, color_mapping
  
imports: import importlib
  import warnings
  from typing import Any, Dict
  import matplotlib
  import numpy
  import packaging
  from matplotlib.colors import to_hex
  from scipy.stats import mode, rankdata
  from scipy.interpolate import CubicSpline
  from ..rcparams import rcParams
  from ..stats.density_utils import kde
  from ..stats import hdi
label_desc: Use image node_id 6 for calling a global function with example usage: color_from_dim(dataarray, dim_name) and returns: colors,  color_mapping